matplotlib-users Mailing List for matplotlib

Hi, I have a plot which is divided into 3x3 subplots, each of
which is generated by imshow.  I now want to do the following
and have difficulties finding the appropriate documentation:

1. Display a single title for the entire plot.  title() will either
    have no effect or attach the title to a separate subplot.

2. Have the same color scale on each subplot.  Is it possible to
    use automatic scaling?

3. Attach a single colorbar to the entire plot, rather than
    9 identical scales to each subplot.

4. Have the labeling of the colorbar use the scientific
    number format.  I sometimes have rather small values,
    and colorbar() will display 0.0 throughout the scale.

I'd be grateful for any hints,
Marcel

I am trying to create plots that look good in color or grayscale (I
had asked about this before and was trying to write code with a switch
- now I just want decent looking plots without having to switch - and
I never really finished the with switching stuff either).

I use the pylab interface and rely a lot on the default behavior for
incrementing my colors when I overlay plots - i.e. I call plot
different times with different data and it automatically makes the
first one blue and the second green, ...

How do I change the default color order and how do I set up a similar
default linetype order, so that the first call to plot generates a
solid line and the second a dashed one (for example).

Thanks,

Ryan

Albert Swart wrote:
>  
> Matlab 's contour function returns the contour data as x- and y- 
> coordinates in a contour matrix C:
>  
> [C,H] = CONTOUR(...)

You are using an old version of Matlab...
>  
> pylab.contour(...) returns a ContourSet object that only seems to 
> contain contour heights.  How do I get the actual contour data?  I need 
> the (x,y) coordinates as given by matlab.  In fact even the binary 
> contour image that is displayed by contour() will be usefull.
>  
> albert

The ContourSet object includes the attribute "collections", a list of 
LineCollections or PolyCollections (for contour and contourf, 
respectively), with one collection per line or color band.  For each 
collection in the list, you can access the vertices using the 
get_verts() method.

Eric

>
>
>>>>"clovis" == clovis  <cl...@pe...> writes:
>>>>        
>>>>
>
>    clovis> All, I followed up the 'memory leak' discussion in the
>    clovis> sourceforge list and I know the Matplotlib-FAQ entry about
>    clovis> this subject. I've also seen John Hunter's post about the
>    clovis> need of matching figure/close pairs.  Anyway, I still feel
>    clovis> that there are problems in this subject, which can be
>    clovis> exposed by the following script (for Windows, but can
>    clovis> easily be adapted to Unix).
>
>    clovis> As can be seen by the results (also given below), there is
>    clovis> a steady increase in memory usage which is not recovered!
>
>If I recall correctly, there is a known leak in tkagg when you create
>multiple canvases, and this is in Tk and not matplotlib proper.  Todd
>may have something to add here.  
>
>JDH
>
>
>  
>
Yesterday you mentioned that that 'memory leak' would probably be caused 
by Tkagg
and not by matplotlib. You also mentioned that it would be good to hear 
Todd Miller
about this subject (I agree).

Following your idea I tested the memory usage under different backends.
The results are given below:

##############
#TKAgg results
##############
Date/time of test   = Wed May 17 10:08:39 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = TkAgg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    15740     21564      5824
Memory usage before/after figure[ 1] =    21564     25944      4380
Memory usage before/after figure[ 2] =    25944     30316      4372
Memory usage before/after figure[ 3] =    30316     34668      4352
Memory usage before/after figure[ 4] =    34668     39020      4352
Memory usage before/after figure[ 5] =    39020     43376      4356
Memory usage before/after figure[ 6] =    43376     47740      4364
Memory usage before/after figure[ 7] =    47740     52096      4356
Memory usage before/after figure[ 8] =    52096     56472      4376
Memory usage before/after figure[ 9] =    56472     60836      4364

############
#Agg results
############
Date/time of test   = Wed May 17 09:24:26 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = Agg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    14160     17008      2848
Memory usage before/after figure[ 1] =    17008     17504       496
Memory usage before/after figure[ 2] =    17504     17604       100
Memory usage before/after figure[ 3] =    17604     17604         0
Memory usage before/after figure[ 4] =    17604     17588       -16
Memory usage before/after figure[ 5] =    17588     17608        20
Memory usage before/after figure[ 6] =    17608     17600        -8
Memory usage before/after figure[ 7] =    17600     17596        -4
Memory usage before/after figure[ 8] =    17596     17584       -12
Memory usage before/after figure[ 9] =    17584     17604        20

##############
#Cairo results
##############
Date/time of test   = Wed May 17 10:29:09 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = Cairo
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    14024     15436      1412
Memory usage before/after figure[ 1] =    15436     15944       508
Memory usage before/after figure[ 2] =    15944     16252       308
Memory usage before/after figure[ 3] =    16252     16460       208
Memory usage before/after figure[ 4] =    16460     16468         8
Memory usage before/after figure[ 5] =    16468     18448      1980
Memory usage before/after figure[ 6] =    18448     18464        16
Memory usage before/after figure[ 7] =    18464     16744     -1720
Memory usage before/after figure[ 8] =    16744     18144      1400
Memory usage before/after figure[ 9] =    18144     18488       344

#################
#GTKCairo results
#################
Date/time of test   = Wed May 17 10:28:45 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = GTKCairo
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    20888     26384      5496
Memory usage before/after figure[ 1] =    26384     26972       588
Memory usage before/after figure[ 2] =    26972     28224      1252
Memory usage before/after figure[ 3] =    28224     27848      -376
Memory usage before/after figure[ 4] =    27848     27440      -408
Memory usage before/after figure[ 5] =    27440     29444      2004
Memory usage before/after figure[ 6] =    29444     29668       224
Memory usage before/after figure[ 7] =    29668     28840      -828
Memory usage before/after figure[ 8] =    28840     28804       -36
Memory usage before/after figure[ 9] =    28804     29884      1080

############
#GTK results
############
Date/time of test   = Wed May 17 09:20:02 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = GTK
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    22128     28044      5916
Memory usage before/after figure[ 1] =    28048     28624       576
Memory usage before/after figure[ 2] =    28624     28804       180
Memory usage before/after figure[ 3] =    28804     28872        68
Memory usage before/after figure[ 4] =    28872     28948        76
Memory usage before/after figure[ 5] =    28948     29020        72
Memory usage before/after figure[ 6] =    29020     29080        60
Memory usage before/after figure[ 7] =    29080     29144        64
Memory usage before/after figure[ 8] =    29144     29240        96
Memory usage before/after figure[ 9] =    29240     29292        52

###############
#GTKAgg results
###############
Date/time of test   = Wed May 17 09:20:24 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = GTKAgg
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    23880     31124      7244
Memory usage before/after figure[ 1] =    31124     31708       584
Memory usage before/after figure[ 2] =    31708     31904       196
Memory usage before/after figure[ 3] =    31904     32308       404
Memory usage before/after figure[ 4] =    32308     32388        80
Memory usage before/after figure[ 5] =    32388     32144      -244
Memory usage before/after figure[ 6] =    32144     32560       416
Memory usage before/after figure[ 7] =    32560     32276      -284
Memory usage before/after figure[ 8] =    32276     32380       104
Memory usage before/after figure[ 9] =    32380     32444        64

############
#EMF results
############
Date/time of test   = Wed May 17 09:18:19 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = EMF
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=True  SAVEFIG=True
Memory usage before/after figure[ 0] =    12352     13268       916
Memory usage before/after figure[ 1] =    13268     13732       464
Memory usage before/after figure[ 2] =    13732     13896       164
Memory usage before/after figure[ 3] =    13896     13900         4
Memory usage before/after figure[ 4] =    13900     13804       -96
Memory usage before/after figure[ 5] =    13804     13904       100
Memory usage before/after figure[ 6] =    13904     13904         0
Memory usage before/after figure[ 7] =    13904     13912         8
Memory usage before/after figure[ 8] =    13912     13836       -76
Memory usage before/after figure[ 9] =    13836     13920        84

###########
#PS results
###########
Date/time of test   = Wed May 17 09:21:15 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = PS
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    12912     14032      1120
Memory usage before/after figure[ 1] =    14032     14508       476
Memory usage before/after figure[ 2] =    14508     16344      1836
Memory usage before/after figure[ 3] =    16344     16548       204
Memory usage before/after figure[ 4] =    16548     17032       484
Memory usage before/after figure[ 5] =    17032     16996       -36
Memory usage before/after figure[ 6] =    16996     16648      -348
Memory usage before/after figure[ 7] =    16648     14876     -1772
Memory usage before/after figure[ 8] =    14876     16928      2052
Memory usage before/after figure[ 9] =    16928     16804      -124

############
#SVG results
############
Date/time of test   = Wed May 17 09:21:34 2006
OS version          = 2.4.3 (#69, Apr 11 2006, 15:32:42) [MSC v.1310 32 
bit (Intel)]
OS platform         = win32
Matplotlib version  = 0.87
Matplotlib revision = $Revision: 1.122 $
Matplotlib backend  = SVG
Column #0 = figure index
Column #1 = memory usage before figure
Column #2 = memory usage after  figure
Column #3 = (after-before) memory

Configuration SHOWFIG=False  SAVEFIG=True
Memory usage before/after figure[ 0] =    12844     13648       804
Memory usage before/after figure[ 1] =    13648     14128       480
Memory usage before/after figure[ 2] =    14128     14224        96
Memory usage before/after figure[ 3] =    14224     14224         0
Memory usage before/after figure[ 4] =    14224     14224         0
Memory usage before/after figure[ 5] =    14224     14228         4
Memory usage before/after figure[ 6] =    14228     14228         0
Memory usage before/after figure[ 7] =    14228     14228         0
Memory usage before/after figure[ 8] =    14228     14228         0
Memory usage before/after figure[ 9] =    14228     14228         0

As can be seen by the previously show results, it seems that the TKAgg 
still has some problems.
I'm not worried the the 'DC level' of the backends or with the results 
concerning figure[0].
The problem I'm pointing is the continuous increase in memory usage in 
the TKAgg backend.
The script used for these tests is:

import pylab
import os
import time
N = 10              # number of loops to execute
SAVEFIG = True      # SAVEFIG execution flag
SHOWFIG = False     # SHOWFIG execution flag

report_filename = 'memory_report_%s.txt' % pylab.matplotlib.get_backend()
fid = file(report_filename,'wt')
fid.write('Date/time of test   = %s\n' % time.asctime())
fid.write('OS version          = %s\n' % os.sys.version)
fid.write('OS platform         = %s\n' % os.sys.platform)
fid.write('Matplotlib version  = %s\n' % pylab.matplotlib.__version__)
fid.write('Matplotlib revision = %s\n' % pylab.matplotlib.__revision__)
fid.write('Matplotlib backend  = %s\n' % pylab.matplotlib.get_backend())
fid.write('Column #0 = figure index\n')
fid.write('Column #1 = memory usage before figure\n')
fid.write('Column #2 = memory usage after  figure\n')
fid.write('Column #3 = (after-before) memory\n')
      
pylab.ion()
a=pylab.arange(0,10)

def report_memory():
    ### Attention: the path to the pslist utility should be adjusted 
according to installation!
    if os.sys.platform == 'win32':
        ps_exe_filename = os.path.join(os.getcwd(),'pslist.exe')    
#Build ps filename
        a = os.popen('%s -m python' % ps_exe_filename).readlines()  
#Build and execute command
        b = a[8]
        c = b.split()
        return int(c[3])
    else:
        print 'Sorry, you have to adapt the command for your OS!'
        return 0

def figureloop(N):
    for i in range(0,N):
        memory_usage_before = report_memory()
        fid.write('Memory usage before/after figure[%2d] = %8d' % (i, 
memory_usage_before))
        pylab.figure(i)
        pylab.plot(a,2*a)
        figurename = 'fig%02d' % i
        if SAVEFIG:
            pylab.savefig(figurename)
            pylab.savefig(figurename+'.eps')
        if SHOWFIG:
            pylab.show()
        pylab.close(i)
        time.sleep(1.0)                 # wait 1.0 second before 
inspecting memory usage
        if os.path.isfile(figurename):  # remove figure ...
            #os.remove(figurename)
            pass
        memory_usage_after = report_memory()
        delta_memory = memory_usage_after - memory_usage_before
        fid.write('  %8d  %8d\n' % (memory_usage_after, delta_memory))
        print '%2d %6d %6d %6d' % (i, memory_usage_before, 
memory_usage_after, delta_memory)
       

print 'Column #0 = figure index'
print 'Column #1 = memory usage before figure'
print 'Column #2 = memory usage after  figure'
print 'Column #3 = (after-before) memory'
print 'There is a sleep time of 1s between each figure!'
print 'Close Figure[0] to continue execution!'
print('\nConfiguration SHOWFIG=%s  SAVEFIG=%s' % (SHOWFIG, SAVEFIG))
fid.write('\nConfiguration SHOWFIG=%s  SAVEFIG=%s\n' % (SHOWFIG, SAVEFIG))
figureloop(N)

This script should be executed with the backend parameters, such as:
    python memory_test.py -dTKAgg
    python memory_test.py -dAgg
    etc
for all the desired backends.

Thanks for your support and congratulations for your great work 
(matplotlib).
clovis

Eric Emsellem <ems...@ob...> writes:

> sorry to interfere here, but it seems that I cannot see any post on
> the forum after the 05/05/2006, including the ones that I am sending. 

FWIW, I saw your post just fine on Gmane
http://news.gmane.org/gmane.comp.python.matplotlib.general
so probably this is a problem with the Sourceforge forum thing.

-- 
Jouni

Hi
sorry to interfere here, but it seems that I cannot see any post on the 
forum after the 05/05/2006, including the ones that I am sending. Going 
to the forum on the web page I don't see them.
( http://sourceforge.net/mailarchive/forum.php?forum=matplotlib-users)

I only received a single digest on 16/05 in the last 2 weeks, but even 
these posts are not on the web. So it seems I am "disconnected" from the 
forum. Is there anything wrong with forum? or with my subscription?

Thanks for any help here and sorry for the inconvenience.

Eric

Matlab 's contour function returns the contour data as x- and y- =
coordinates in a contour matrix C:

[C,H] =3D CONTOUR(...)

pylab.contour(...) returns a ContourSet object that only seems to =
contain contour heights.  How do I get the actual contour data?  I need =
the (x,y) coordinates as given by matlab.  In fact even the binary =
contour image that is displayed by contour() will be usefull.

albert

Hi,

I would like to be able to make a scatter plot using a new symbol which 
I would draw using whatever drawing software (so a sketched star, or a 
saturn like symbol...) and export in some proper format to be used by 
matplotlib.
Is there a way to do this?

thanks for any help here!

Cheers
Eric